Jaydeep Karandikar Senior R&D Staff Contact 865.574.4641 | KARANDIKARJM@ORNL.GOV All Publications Bayesian stability and force modeling for uncertain machining processes Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data Iterative Stress Reconstruction Algorithm to Estimate Three-Dimensional Residual Stress Fields in Manufactured Components Visualization and data analytics for optimal process parameter selection for turning Milling tool wear Chatter detection in simulated machining data: a simple refined approach to vibration data Residual stress accumulation in large-scale Ti-6Al-4V wire-arc additive manufacturing Process damping identification using Bayesian learning and time domain simulation Review of in situ process monitoring for metal hybrid directed energy deposition... Using GANs to predict milling stability from limited data Integration of discrete-event dynamics and machining dynamics for machine tool: Modeling, analysis and algorithms Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements Predicting chatter using machine learning and acoustic signals from low-cost microphones Physics-informed Bayesian machine learning case study: Integral blade rotors Evaluation of automated stability testing in machining through closed-loop control and Bayesian machine learning Process window estimation in manufacturing through Entropy-Sigma active learning Low-cost sensors and analytics for a digital factory Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability Bayesian optimization for inverse calibration of expensive computer models: A case study for Johnson-Cook model in machining INTEGRAL BLADE ROTOR MILLING IMPROVEMENT BY PHYSICS-GUIDED MACHINE LEARNING REMOTE BAYESIAN UPDATING FOR MILLING STABILITY Logistic classification for tool life modeling in machining... A Bayesian Framework for Milling Stability Prediction and Reverse Parameter Identification Estimating Johnson-Cook Material Parameters using Neural Networks Propagation of Johnson-Cook flow stress model uncertainty to milling force uncertainty using finite element analysis and time domain simulation Pagination Current page 1 Page 2 Next page ›â¶Äº Last page Last » Key Links Organizations Energy Science and Technology Directorate Manufacturing Science Division Precision Manufacturing and Manufacturing Innovation Advanced Machining and Machine Tools Research User Facilities Manufacturing Demonstration Facility
